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Credit Risk Scorecard

Credit risk modelling is an approach to determine the probability of default of a customer. Credit scoring is based on analysing customer’s personal information, transactional information, last premiums and payments made and many more variables and the outcome is a score reflecting the creditworthiness of the customer.

There are various credit scoring techniques which can be used for building collection scorecard, behavioural scorecard, application scorecard, cross-sell and upsell models, customer acquisition and retention strategies.

Demand Forecasting, Customer Segmentation, Higher Campaign ROI, Predictive Attrition Modelling, Cross-Sell Modelling

Impact

Improved strategies across all functions

Reduced credit losses.

Enable decision making at different stages of customer lifecycle.

Strengthen enterprise-wide compliance programs.

What we do

Data Sourcing and Data Pre-processing
  • Understand the data schema and data dictionary.
  • Access data quality gaps and data sufficiency
  • Perform vintage and roll-rate analysis of needed.
Data Transformation
  • Perform variable transformation.
  • Correlation tests for multicollinearity.
  • Feature selection based on variable significance and information value.
Model Fitting & Validation
  • Model methodology specification
  • Model implementation
  • Build machine learning risk scoring classifier model
Risk Scorecard Validation
  • Carry out scorecard validation and out pf time validation
  • Perform reject inferencing, if needed
Monitor Model performance
  • Review Model approach
  • Access model implementation
Want to learn more about TransOrg’s value proposition, solution methodology and implementation approach?

Case Studies

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